Abstract

Oil is produced at the Horn Mountain field (Gulf of Mexico, Mississippi Canyon blocks 126 and 127) from middle Miocene reservoirs M and J. Reservoir facies are characterized as sand-filled channels and associated overbank deposits and are positioned in combination structural and stratigraphic traps. Prior to initial production, several barriers and baffles were identified in both reservoirs by integrating geological, geophysical, petrophysical, pressure, PVT (pressure-volume-temperature relationships), and geochemical data and petroleum-filling history. A compartmentalization risk matrix was developed to facilitate and visualize the integrated evaluation of compartmentalization. During production, in addition to traditional surveillance technologies, we applied time-lapse geochemistry (TLG) to visualize petroleum sweep by monitoring changes in fluid composition and fingerprints across reservoirs. In this technology, appraisal and preproduction fluid samples are first analyzed to map fluid types across a static reservoir. Then, a surveillance program in which fluid samples are taken from producing wells at regular time intervals is designed and executed. The obtained production samples are geochemically fingerprinted and compared with preproduction fluids from the same well and surrounding wells. At Horn Mountain, interpretation of geochemical data allowed us to infer oil movement across reservoir M and helped to reevaluate reservoir models and reduce risks in managing reservoir performance. In reservoir J, an untapped compartment was identified, and an additional producer was justified for future drilling. Time-lapse geochemistry results were consistent with and complimentary to other surveillance data available to date. Our study demonstrates that TLG is a safe and cost-effective technology, which reduces uncertainties associated with other reservoir surveillance methods and appears to be valuable for reservoir management.

Alexei Milkov is a petroleum systems analyst for BP. He holds degrees (B.Sc. degree, 1996; M.Sc. degree, 1998) in geology from Saint Petersburg State University (Russia) and Texas A&M University (Ph.D., 2001). Alexei uses his basin modeling and organic geochemistry skills to assist in exploration, appraisal, development, production, and environmental projects around the world. He is an adjunct professor at Cornell University.

Evvy Goebel works as a development and production geologist for BP's Gulf of Mexico Deepwater Production business unit. She earned a B.Sc. degree in geology from Marietta College and an M.Sc. degree in geology from the University of Cincinnati. Evvy has more than 20 years of experience in exploration, development, and production in North America. She is a licensed professional geologist in Texas.

Leon Dzou is a geosciences advisor and a member of the Exploration Excellence Team with BP in Houston. He has 20 years of industry experience. After joining BP in 1997, Leon provided petroleum systems support in exploration, appraisal-development, production, and environmental projects globally. Leon received his Ph.D. in organic geochemistry from University of Texas in Dallas (1990).

David Fisher is a geophysicist for BP's Gulf of Mexico Deepwater Production business unit. He earned a B.A. degree in geology from the University of California–Santa Barbara and an M.Sc. degree in geophysics from the University of Houston. David has worked for BP(ARCO) since 1983, specializing in three-dimensional seismic interpretation techniques applied to appraisal, development, and production projects worldwide.

Allen Kutch works as reservoir engineer for BP's Gulf of Mexico Deepwater Production business unit. He earned a B.Sc. degree in petroleum engineering from Texas A&M University and an M.Sc. degree in petroleum engineering from the University of Houston. Allen has 15 years of production and reservoir experience in the Gulf of Mexico and onshore lower 48 states. He is a licensed professional engineer in Texas.

Neal McCaslin is a senior reservoir engineer in BP's Gulf of Mexico Deepwater Production business unit. He earned a B.S. degree from the University of Texas at Austin in 1985 and has more than 20 years of industry experience. Neal has worked exploration, appraisal, development, and production for ARCO Oil and Gas Company, Vastar Resources, and BP America. He has spent most of his career working in the Gulf of Mexico with the last 10 years exclusively in the deep water.

Dave Bergman is a fluid PVT consultant for BP, providing quality assurance and quality control for laboratory studies, developing equation of state descriptions for reservoir and facility modeling, and teaching fluid PVT and equations of state courses. He has a B.S. degree in chemical engineering from Michigan Technological University, and a master's degree and a Ph.D. in chemical engineering from the University of Michigan. He has worked for BP (Amoco) since 1976.

INTRODUCTION

Reservoir surveillance is a key activity during production of oil and gas from reservoirs. The main objective of reservoir surveillance is to understand petroleum fluid behavior (e.g., phase changes) and movements within a reservoir during production. Surveillance data and interpretations are applied mainly to validate and update preproduction reservoir models, understand compartmentalization, predict and optimize reservoir performance, manage well placement and interventions, and locate unswept parts of reservoirs. The petroleum industry deploys a variety of technologies for reservoir surveillance. The most commonly used technologies include interpretations of individual well production histories, pressure and transient analysis, temperature logging, production logging, interference testing, tracer analysis, and in more recent years, interpretation of four-dimensional (4-D) (time-lapse) seismic data (Bradley et al., 1986; Hower and Collins, 1989; Janssen, 1992; Cervantes, 1996; Ali et al., 2000; Shenawi et al., 2000; Chopra and McConnell, 2004; Govan et al., 2005; Hudson et al., 2005; Jemmott, 2005).

In this article, an application of time-lapse geochemistry (TLG), a novel surveillance technology to monitor petroleum sweep, is described for two reservoirs of the Horn Mountain field in the deep-water Gulf of Mexico. First, we constructed integrated reservoir compartmentalization models based on geological, geophysical, petrophysical, pressure, PVT (pressure-volume-temperature relationships) and geochemical preproduction data, and petroleum charge (filling) model for the field. Variations in geochemical compositions and fingerprints of oils revealed during appraisal and development were mapped and served as baseline information for TLG. We then monitored how compositions and fingerprints of produced oils changed through time during production and inferred oil movements within reservoirs. Results from TLG, when integrated with other surveillance technologies, provided valuable information on reservoir performance and allowed us to validate reservoir models and locate unswept parts of the field.

HORN MOUNTAIN FIELD

Horn Mountain field is situated in the Mississippi Canyon (MC) blocks 126 and 127 (Gulf of Mexico, ∼180 mi [∼290 km] southeast of New Orleans) at water depth of about 5400 ft (1646 m) (Figure 1a). The field was discovered in July 1999, and first production started in November 2002. Approximately 71 MMBOE were produced by the end of February 2006. Production from two reservoirs by eight wells peaked at approximately 70,000 BOEPD in mid-2003 and came off plateau in May 2004. Sustaining production from the Horn Mountain facility is the main current challenge, which is being met through the optimization of the existing production as well as reserve growth by identifying parts of the field that currently are not contributing to the production.

Oil pay zones are located between approximately 12,200 and 14,200 ft (3719 and 4328 m) true vertical depth subsea (TVDSS) in M (deeper) and J (shallower) structurally and stratigraphically complex Miocene reservoirs (Figure 1b). Well logs indicate the presence of gas in a thin reservoir K located between reservoirs M and J, but this reservoir is not areally extensive, and there is no current production from it. Oil accumulations in reservoirs M and J are trapped by a major west-east (WE) fault dipping to the north and stratigraphic pinch-outs in the eastern and northeastern parts of the field (Figure 1b–d). In addition to the discovery well BP MC127#1, 8 appraisal and 11 development wells (including sidetracks) penetrated the pay zones. A wealth of subsurface information was collected before production, including three-dimensional (3-D) seismic data, well logs, cores, pressure measurements, and fluid data. These data were interpreted to construct integrated reservoir compartmentalization models for reservoirs M and J.

METHODS

Seismic data, well logs, and cores were collected and analyzed according to standard industry practices. Pressure measurements were made with the Schlumberger Modular Formation Dynamics Tester (MDT) tool. The quartz gauge of an MDT is accurate to within 2 psi (13.7 kPa) + 0.01% of pressure and has a resolution of 0.003 psi (0.02 kPa). PVT properties (API gravity, gas/oil ratio [GOR], saturation pressure [Psat], and others) of fluids collected by MDT were measured at Oilphase (Houston, Texas). Because all MDT fluid samples had various (0.3–10.6 wt.%) oil-base mud (OBM, PetroFree) contamination, in-house algorithms were used to decontaminate PVT properties.

Geochemical measurements on dead (i.e., degassed at atmospheric conditions) oil samples were performed at Baseline (Shenandoah, Texas) unless mentioned otherwise. Sulfur (S) concentrations were measured on a LECO SC132 sulfur analyzer (accuracy and precision ±1% of sulfur content). Nickel (Ni) and vanadium (V) were determined by inductively coupled plasma atomic emission spectrometry (method ASTM D 5708) at Texas OilTech Laboratories (Houston, Texas) (accuracy and precision ±1 ppm). The saturate-aromatic fraction of oils was separated from the resin-asphaltenes fraction through precipitation by n-pentane. Saturate and aromatic fractions were further separated by medium-pressure liquid chromatography, in which hexane flows through a silica column and elutes saturates first and aromatics second. The carbon isotopic composition (reported as δ13C values) of saturates and aromatics were measured by isotope ratio mass spectrometry (IRMS) (accuracy and precision ±0.15‰).

Whole oil gas chromatography (WOGC) was performed on an Agilent 6890NT gas chromatograph (GC) equipped with a DB-1 capillary column (60 m × 0.25 mm [196 ft × 0.009 in.] inner diameter) and flame ionization detector (FID) set at 350°C (662°F). Helium was used as a carrier gas flowing at a rate of 1.6 mL/min. Column temperature was initially set at 30°C (86°F) (for 5.0 min) and then was programmed to increase to 320°C (608°F) at a rate of 3°C/min (37.4°F/min) and was finally held for 20 min, so that the total run time was 122 min. Biomarkers in saturate and aromatic fractions were measured by GC–mass spectrometry system comprising an Agilent 6890 GC interfaced to an Agilent 5972 mass selective detector quadrupole mass spectrometer (Peters et al., 2005).

All geochemical measurements on gases were performed at Isotech Laboratories (Champaign, Illinois). The molecular composition of natural gases in IsoTube and MDT samples was measured on a regular GC system (Agilent 6890) with flame ionization (FID) and thermal conductivity detectors. The carbon isotope composition of gases in IsoTubes samples was measured using an online IRMS system, where an Agilent 6890 GC and Finnigan GCCIII combustion unit are interfaced to a Finnigan Delta+ mass spectrometer. The hydrocarbon components were separated by the GC, and each individual component slated for isotopic analysis was combusted. The resultant carbon dioxide (CO2) was introduced directly into the mass spectrometer. An offline preparation system and a dual-inlet mass spectrometer were used for carbon and hydrogen isotope measurements on gases derived from MDT samples. In that offline system, customized Gow Mac GCs are interfaced with a vacuum and combustion system to separate the hydrocarbon of interest from the other components and then combust the component into CO2 and water. The resultant CO2 and water are purified on the vacuum system and then sealed into Pyrex tubes for isotopic analysis. The CO2 is analyzed directly on a dual inlet mass spectrometer such as Finnigan Delta S. The water is reacted with zinc turnings and converted to hydrogen gas, which is then analyzed on the Delta S. The carbon isotope composition of gases is expressed in δ13C values, which are reported as per mil (‰) relative to the Peedee belemnite standard (accuracy and precision, ±0.1‰ [offline method] and ±0.3‰ [IRMS]). The hydrogen isotope composition of gases is expressed in δD values, which are reported as per mil (‰) relative to standard mean ocean water (precision better than ±2‰).

Reservoir M

Stratigraphy and Structure

Reservoir M is the main (82% of reserves), larger, deeper reservoir in the Horn Mountain field. The reservoir is composed of interbedding sands, silts, and mudstones deposited in relatively clean channels and associated laminated levees and overbanks (Vernik et al., 2002) during the lower part of a sea level lowstand in the lower to middle Miocene (Figure 1b, c). True vertical thickness of reservoir M ranges from 109 ft (33.2 m) to 398 ft (121.3 m), and the net-to-gross (N/G) ratio ranges from 35% (distal levee facies) to 69% (proximal levees) to as much as 84% (channel-fill facies). Porosity ranges from 14% to 34% and averages about 27%. Permeability shows a wide range, from 2 to 6130 md, and has a strong facies control, with average values of 183 md in distal levees, 309 md in proximal levees, and 1360 md in channel-fill sandstones. A meandering shale-filled bypass channel that developed to the east of the reservoir provides the eastern stratigraphic boundary to the oil accumulation (Figure 1c). Shale channels also occur within the reservoir.

Structurally, reservoir M forms a gentle anticlinal fold plunging to the southwest (Figure 1c). A major normal fault trends from east to west, dips to the north, and serves as the northern sealing boundary for the oil accumulation. This fault has a maximum offset of 1700 ft (518 m) at the level of reservoir M and is related to the movement of a deep salt body. Several smaller faults separate reservoir M into northern, central, and eastern fault blocks (NFB, CFB, and EFB in Figure 1c). The actual oil-water contact (OWC) was not penetrated in any well and is assumed to be at approximately 14,300 ft (4359 m) TVDSS in the main part of the reservoir and about 14,200 ft (4328 m) TVDSS in the western part of the reservoir based on the highest known water (14,257 ft [4346 m] in well BP MC126#1ST2) and the lowest known oil (14,187 ft [4324 m] in well BP MC126#1).

Pressure Data

Pressure data measured by downhole tools such as MDT are commonly used to infer the presence of barriers/baffles in a reservoir (Pelissier-Combescure et al., 1979). Although advantages of detailed advanced interpretation based on excess pressures have been recently proposed (Brown, 2003), we applied a more conventional approach in which pressure measurements are plotted against the depth (TVDSS). Pressure data within the oil column in reservoir M do not fit a single gradient. Instead, two gradients can be distinguished (Figure 2). The larger gradient (0.310 psi/ft; 7.0116 kPa/m) is observed in the bottom part of the column below approximately 13,500 ft (4115 m) TVDSS, and a slightly smaller gradient (0.297 psi/ft; 6.71 kPa/m) is observed in the upper part of the column.

Select pressure data from reservoirs M and J in the Horn Mountain field. All pressure measurements were made with the Modular Formation Dynamics Tester (MDT). Error bars of measurements are smaller than the size of symbols.

The difference between the uppermost measured pressure and the pressure extrapolated upward from measurements in the bottom part of the column is approximately 20 psi (137 kPa), which is significantly larger than the error of measurements (less than 3 psi; 20.6 kPa). Such a large difference may be interpreted as indicating pressure (and fluid) barriers that prevent pressure equilibration between the upper and lower part of the oil column. Contrary to that, we infer that the observed different pressure gradients result from differences in fluids, which, in their turn, result from a complex petroleum-filling history. Fluids in the upper part of the column contain more dissolved microbial gas than fluids in the lower part of the column. This results in higher GOR, lower fluid density, and therefore, smaller pressure gradient in the upper part of the column. The intersection of two pressure gradients occurs at about 13,500 ft (4115 m) TVDSS, which corresponds to the depth where both the GOR and microbial methane concentrations start rising toward the top of the oil column as discussed below.

Basic PVT Properties of Fluids

Reservoir M contains oil with dissolved gas. Saturation pressure (Psat) in the reservoir shows a large gradient: oils at the bottom of the column are significantly (by as much as ∼4250 psi [∼29.3 MPa]) undersaturated with gas, whereas oils at the top of the column are almost saturated with gas (Figure 3a). The GOR (decontaminated, calculated for ideal conditions at 60°F [15.5°C]) values remain almost constant in the lower part of the oil column (below ∼13,500 ft [∼4115 m] TVDSS), where they are about 800 scf/stb (Figure 3b). However, a significant upward increase in GOR is observed between about 13,500 ft (4115 m) and the top of the column, where GOR reaches its maximum values at about 1400 scf/stb. This increase is a result of a more significant contribution of dissolved microbial gases (mostly methane) in the upper part of the reservoir, as is inferred from carbon stable isotope data in gases (see below).

Pressure-volume-temperature (PVT) data from reservoirs M and J in the Horn Mountain field. (a) Saturation pressures have errors bars equal to 5% of measurements. Reservoir pressure data are approximated by dashed lines, and exact values can be found in Figure 2. (b) Plotted GOR values were calculated for decontaminated fluids at ideal conditions. Values have error bars equal to 5% of measurements. (c) Plotted API gravity values were calculated for decontaminated fluids at ideal conditions. Values have error bars equal to 1°.

The API gravity (decontaminated, calculated for ideal conditions at 60°F [15.5°C]) of dead oils ranges from 31 to 36° API (Figure 3c). No clear trend in variations of API gravity with depth exists, although the values may slightly increase at the bottom of the oil column. A negative correlation exists between API gravity and GOR of reservoir fluids (Figure 4). These somewhat unusual (Stainforth, 2004) but commonly observed in the Gulf of Mexico PVT properties result from a complex filling history that encompasses a preexisting microbial methane accumulation and continuously arriving oil charges of increasing maturity.

Strong negative correlation between GOR and API gravity in reservoirs M and J in the Horn Mountain field. Error bars are the same as in Figure 3.

Variations in Geochemical Composition and Fingerprints of Petroleum Fluids

All oil samples obtained in appraisal and development wells from MDT cylinders were contaminated with OBM. Therefore, the concentrations of sulfur, vanadium, nickel, stable carbon isotopic composition of saturate and aromatic fractions, and molecular ratios pristane/phytane (Pr/Ph), Pr/n-C17, and Ph/n-C18 are reported in Table 1 for the first available production samples. A typical noncontaminated WOGC from reservoir M is shown in Figure 5a. Because OBM does not contain biomarkers, the relative concentrations of biomarkers and calculated biomarker indices are presented in Table 1 for both the MDT and production sample.

Typical WOGC of production oils from reservoir M (a) and J (b). Inserts show peaks in the n-C8.5–n-C11.5 range (outputs from ReserView software; the same peaks in a and b have the same colors) used for WOGC fingerprinting.

Variations in Fluid Characteristics in Reservoirs M and J in the Horn Mountain Field

Several oil groups were identified and mapped across the reservoir based on oil fingerprinting of interparaffin peaks in the uncontaminated WOGC n-C8.5–n-C11.5 (Figure 5) range. In our fingerprinting work, we followed the methodology developed by Kaufman et al. (1990, 1997) and used the ReserView software (a product of Infologic, Inc., Shenandoah, Texas) to generate data sets of peak heights and ratios. Preproduction and first available production oils from most of the wells appear to have similar fingerprints. However, updip oils in the CFB and the EFB of the reservoir have WOGC fingerprints different from those in the NFB and areas close to the OWC (Figure 6a, b).

Oil groups identified in reservoir M (a and b) and reservoir J (c and d) based on WOGC fingerprinting. Oil samples include preproduction MDT samples from exploration and appraisal wells and first available production samples from wells A1–A5 and A8–A10. Star plots display 12 most different ratios of interparaffin peaks in the noncontaminated n-C8.5–n-C11.5 range and show how oils from different wells group together. Different oil groups have different colors (colors in a are the same as in b, and colors in c are the same as in d). Peak ratios in (b and d) (and in all other star plots in this article) are normalized to mean values for the entire data set in each star plot to visually maximize differences between fingerprints.

Little variations exist in oil compositions in reservoir M. Based on relatively low concentrations of sulfur and metals, Pr/Ph ratios (Table 1), and the relative abundance of hopanes (with dominating C30; Figure 7a, c) and terpanes (C26 > C24tet; Figure 8a, c), we interpret that the oils were generated predominantly from marine shale (class B of Pepper and Corvi, 1995) organofacies (Peters et al., 2005). Little variations in source-specific geochemical parameters exist, and it appears that all oils in the M reservoir are related to one source rock or several source rocks of similar organofacies. However, maturity parameters such as C20/(C20 + C28) triaromatic steranes (Peters et al., 2005) and MDR (4-methyldibenzothiophene [MDBT]/1-MDBT; Radke, 1988) indicate that oils of different maturities are present in the reservoir (Figure 9). In general, the least mature oils occur updip in the CFB (wells MC127#2, BP MC127#3, and A8) and in the EFB (well BP MC127#2ST3) of the reservoir. It appears that differences in oil fingerprints from the WOGC C8.5–C11.5 range (Figure 6a, b) result mostly from differences in maturity across reservoir M.

Distribution of hopanes in oils from reservoirs M and J in the Horn Mountain field. Typical mass chromatograms (mass to change ratio [m/z] = 191) are shown for two samples from reservoirs M (a) and J (b). The plot of calculated relative abundances of individual C29–C35 hopanes in total hopanes (c) shows typical hopane profiles for reservoir M (solid line, same sample as in a) and for reservoir J (dashed line, same samples as in b), as well as the total range of profiles measured in the field (dark-gray area). We interpret that variations in hopane profiles are not very significant, and they indicate that oil originated from similar marine shale organofacies.

Distribution of terpanes in oils from reservoirs M and J in the Horn Mountain field. Typical mass chromatograms (m/z = 191) are shown for two samples from reservoirs M (a) and J (b). The plot of calculated relative abundances of individual C19–C30 terpanes in total terpanes (c) shows typical terpane profiles for reservoir M (solid line, same sample as in a) and for reservoir J (dashed line, same sample as in b), as well as the total range of profiles measured in the field (dark-gray area). We interpret that variations in terpane profiles are not very significant, and they indicate that oil originated from similar marine shale organofacies.

Plot of maturity-specific biomarker ratios for the Horn Mountain oils. Maturity of oils increases with increasing MDR (Radke, 1988) and triaromatic steranes ratio C20/(C20 + C28) (Peters et al., 2005). In addition to oils from MDT cylinders, data for first available produced oils are also plotted and labeled as A1–A5 and A8–A10.

In recombined PVT samples, methane (C1) composes 66–87 mol% of total methane through pentane (C1–C5) gases. Concentrations of gases do not vary significantly in the lower part of the column below about 13,500 ft (4115 m), where C1 accounts for 66–70 mol% of C1–C5 gases in good-quality samples (Figure 10a). However, the concentration of C1 increases (Figure 10a), and concentrations of C2–C5 gases decrease in the upper part of the accumulation (Figure 10b). Carbon isotope data indicate that dissolved C1 in the upper part of the reservoir is enriched in 12C (Figure 11a). The values of δ13C of C1 are as negative as −66.2‰ at the top of the column, but gradually increase to about −58‰ at approximately 13,500 ft (4115 m) TVDSS and then do not change much in the lower part of the column. We interpret that this distribution of carbon isotope values is a result of more abundant microbial C1 (commonly enriched in 12C relative to thermogenic C1, Schoell, 1983) at the top of the column. In contrast, δ13C of C2–C5 gases do not change significantly through the column, except perhaps for the very top sample where isotope values slightly decrease (e.g., see data for δ13C of propane [C3] in Figure 11b). This suggests that C2–C5 gases have relatively similar maturities through the oil column in reservoir M, although C2–C5 gases from the very top of the column may be the least mature.

Concentrations of C1 (a) and C2–C5 (b) gases in total C1–C5 gases in fluids from the Horn Mountain field. Plotted concentrations were calculated as the percent of total C1–C5 in recombined PVT samples.

Carbon isotopic composition (δ13C) of C1 (a) and C3 (b) in fluids from the Horn Mountain field. These data include measurements from MDT, IsoTube, and separator samples. Error bars for most isotope measurements equal to 0.2‰ and are smaller than the size of symbols for C1 (a). Error bars for depth are shown only for separator samples and correspond to the perforated intervals in producing wells.

When δ13C values of C1–C5 gases are plotted on the natural gas plot (Chung et al., 1988), the gases display profiles typical for the Gulf of Mexico (Chung et al., 1988; Weissenburger and Borbas, 2004) and indicative of mixed thermogenic and microbial gases (Figure 12). Using the methodology described by Chung et al. (1988), we estimate that most of the pure thermogenic C1 in the reservoir has δ13C values around −45‰, and the uppermost C1 has lower maturity and δ13C value of −48‰. However, the actual C1 has δ13C values between −58‰ and −66‰. This is because all gases have a part of microbial C1 for which we infer a δ13C value of about −68‰ based on data from pure microbial gas fields in the Mississippi Canyon protraction area. Hydrogen isotope data for separator gases from the first available produced petroleum fluids further confirm that gases in reservoir M have mixed microbial and thermogenic origins (Figure 13). Results of mathematical unmixing (Chung et al., 1988) suggest that total gas in reservoir M contains about 37–81% of microbial C1. A strong (R2 = 0.89) positive correlation exists between the portion of microbial C1 in total gas and GOR (Figure 14).

Carbon isotopic composition (δ13C) of C1–C5 gases in the Horn Mountain field displayed on the natural gas plot (Chung et al., 1988). Dashed lines show extrapolation of carbon isotope values of C2–C5 gases to obtain the δ13C of pure thermogenic C1 in gases (range from −48 to −45‰). Precision and accuracy of isotope measurements are ±0.1‰, which approximately corresponds to the thickness of lines.

Carbon (δ13C) and hydrogen (δD) isotopic composition of C1–C5 separator gases from producing wells (first available samples) in the Horn Mountain field displayed on the interpretation plot simplified from Schoell (1983). Precision and accuracy of isotope measurements are ±0.1‰ for δ13C and ±2‰ for δD, which are much smaller than the size of symbols. Dashed line shows a mixing line between samples with a relatively high (e.g., A10) and relatively low (e.g., A1) amount of microbial C1 in total C1.

Strong positive correlation between GOR and the portion of microbial C1 in total C1–C5 gases in MDT samples from the Horn Mountain field. This correlation served as a key argument for our proposition that GOR in the upper parts of the M and J oil columns is relatively high because the first-arrived oil dissolved the preexisting microbial gas at the crest of the structure.

Petroleum Charge (Filling) History

As discussed above, microbial C1 is an important component of petroleum in reservoir M. The greatest concentrations of microbial C1 in oils are observed at the top of the column (Figure 11), where microbial C1 apparently contributes to the relatively high GOR (Figure 14). We propose here that microbial gas formed in shales surrounding the reservoir in the geological past when subsurface temperatures were optimal (30–60°C; 86–140°F) for microbial activity (present-day reservoir temperature is above 80°C [176°F]). This microbial gas then migrated into adjacent reservoir sands and accumulated in structurally high areas of the reservoir M trap before the oil charge arrived (Figure 15a).

Schematic petroleum charge history for the Horn Mountain field. Panels (a–c) demonstrate the presence of microbial gas prior to oil charge (a), spill of oil around fault tips in reservoir M (b), and leakage to reservoir J (c) in a cross-sectional view along the strike (location of cross section is in panel d). Panels (d–f) show structure and facies (shales in gray, sandstones in yellow) map of reservoir M and demonstrate how the early oil filled the CFB and spilled to the EFB (d), then spilled to the NFB (e), built the column to the present-day OWC and leaked to reservoir J from the crest of the structure within the migration chimney (f). Oil backfilled reservoir J from the crest of the structure downdip in each individual fault block (g). Oil migration in (d–g) was simulated using Trinity (ZetaWare, Houston, Texas) software.

Oil arrived to reservoir M from deep source rocks, which are believed to be Cretaceous and Jurassic (Hood et al., 2002) marine shales. The oil migration in the mudstone section below the reservoir is focused by a deeper salt body in such a way that most petroleum enters reservoir M to the south of the present-day OWC (Figure 15d). The first-arrived oil had a relatively low maturity and was undersaturated with associated thermogenic gas. This oil migrated updip within the carrier bed into the CFB (Figure 15a, d), which was numerically modeled using a structural map of the reservoir (believed to be largely representative of the structure at the time of charge) and the distribution of lithologies controlling migration pathways (Figure 15d). Results of migration modeling are consistent with geochemical observations that the least mature petroleum is found in the CFB (Figure 9). Undersaturated oil dissolved the preexisting microbial gas (Figure 15b) and obtained a relatively high GOR.

We consider in this study that faults within the reservoir are relatively impermeable and serve as barriers for petroleum migration. When more mature thermogenic petroleum arrived, it completely filled the CFB and spilled first into the EFB and then into the NFB (Figure 15b, d, e). These inferences from numerical modeling of migration are broadly consistent with the distribution of oil maturities inferred from biomarker data: the CFB (wells MC127#2 and MC127#3) has the least mature oil; the EFB (well MC127#2ST3) has a higher maturity; and the NFB (e.g., well BP MC127#1) and areas around the present-day OWC have the highest maturity in the field. Oil that dissolved the microbial gas previously accumulated in fault blocks. Continuous arrival of more mature oil led to a buildup of a relatively large (∼2200 ft [∼671 m]) oil column with the present-day OWC at about 14,300 ft (4359 m) (Figure 15f). Because the charge is relatively recent (since ∼6 Ma) and is likely ongoing (on a geological time scale), there was probably not enough time for petroleum to mix and equilibrate well within the reservoir (Smalley et al., 2004). Shale and silt layers common in the reservoir (Figure 1c) further impeded a complete mixing of early-arrived petroleum at the top of the column and later arrived petroleum at the bottom of the column. Therefore, many variations in fluid properties, such as the relatively high GOR (Figure 4b) and relatively low maturity of oil (Figure 9) and, possibly, of thermogenic gas (Figure 11b) at the top of the column, are inferred to be a result of charge history.

When the oil column in M reservoir became sufficiently large, the buoyancy pressure of petroleum exceeded the capillary entry pressure of the top seal, and oil leaked into the upper smaller reservoir J (Figure 15c, g). This interpretation is supported by geochemical data from reservoir J discussed below. Oil did not charge reservoir K, which is thin and has localized distribution, because it is not present directly above the structural high of reservoir M and is off the oil-migration pathways (migration chimney) from reservoir M to reservoir J.

Compartmentalization Model

For the purpose of this study, the available data sets were integrated via a CRM. In this approach, the risks of lateral fluid flow barriers between two adjacent wells are first assigned based on conclusions from each individual data set (e.g., pressure or geochemical data) according to the traffic light methodology. If data suggest that there is a low risk of fluid-flow barriers, then the wells have a green cell in the CRM (Figure 16a). If the presence of fluid-flow barriers between wells is inferred with a high degree of certainty, then the cell is red. Finally, if the interpretation of the data is inconclusive for some reason, then the cell is yellow. The risks also can be displayed on a map by connecting adjacent wells by a green, red, or yellow link. Numerical values and weightings can be assigned to risks, but quantitative treatment of individual and cumulative risks is complex and is beyond the scope of this article.

Compartmentalization risk matrix (a) and final compartmentalization model (b) for reservoir M of the Horn Mountain field. Appraisal wells are identified with “#,” and only well numbers are shown in (b). Full names of appraisal wells include block number (MC126 or MC127) as listed in (a) (for example, well #2 in b has full name MC127#2 in a). Flow barriers in (b) are interpreted to exist between wells with high (red) common risk of barriers in (a). Baffles are interpreted to exist between wells with moderate (yellow) common risk of barriers in (a) and when obvious, and extensive dim zones exist between these wells on the seismic amplitude map.

When assigning high, moderate, or low risk of fluid flow barriers in the CRM, one needs to account for measurement and interpretation uncertainties associated with each individual data set. In some cases (for example, when considering accuracy and precision of oil fingerprinting and pressure data), this can be achieved through duplicate measurements and consideration of error bars. In other cases, a large uncertainty in conclusions remains until a focused study attempts to significantly reduce it. For example, a fault interpreted from seismic data inherently associates with a moderate risk of fluid-flow barriers, unless a special analysis (e.g., a study of shale gauge ratios) determines if the fault is (and/or will be during production) sealing or transmitting.

Petroleum charge is ongoing (on a geological time scale) in many reservoirs within modern continental margins (e.g., Gulf of Mexico, offshore Angola, etc.). More mature fluids that recently (on geological time scale) arrived into these reservoirs have not had enough time to fully mix with the early arrived fluids, especially in stratigraphically and structurally complex turbidite reservoirs with restrictive connectivity (e.g., Weissenburger and Borbas, 2004). In biodegraded reservoirs (e.g., many oil fields in Alaska), the composition of fluids constantly changes because of ongoing microbial activity. Fluids in such accumulations have not reached a steady-state equilibrium condition. Therefore, significant variations in PVT and geochemical properties (including WOGC fingerprints) may result from charge and postaccumulation history accompanied by an insufficient time for fluid mixing instead of fluid flow barriers and baffles (Westrich et al., 1999; Wavrek et al., 2001). Fluid properties vary significantly in reservoir M, but most variations can be explained by involving a geologically reasonable charge model, which was tested with basic petroleum migration modeling tools (Figure 15). Therefore, we concluded that most differences in fluids resulted from filling history and incomplete mixing. High risk of flow barriers was assigned only between wells where fluid variations were opposite to what was expected from the simple model of increasingly mature oil charging well-connected reservoir with preexisting microbial gas cap.

The final prediction of lateral reservoir connectivity between two wells is derived based on the worst case scenarios from individual data sets. A green cell in the common risk column of the CRM means that wells likely do not have fluid flow barriers between them and will communicate during production. A red cell means that there are likely barriers between wells, and the wells likely would not communicate during production. A yellow cell means that baffles may occur between the wells, and these baffles may or may not transmit fluids during production.

Based on the preproduction data and accounting for both uncertainties in individual data sets and petroleum charge history, we inferred that faults likely serve as barriers in reservoir M. Additionally, there are flow baffles formed by shale-filled channels and transitions from sand channels to levees and overbanks. These baffles transmitted petroleum during reservoir filling and may allow fluid transmission during production. The final compartmentalization model for reservoir M is presented in Figure 16b.

Reservoir J

Stratigraphy and Structure

Reservoir J is the smaller, shallower producing interval in the Horn Mountain field. Similar to reservoir M, the pay zone facies were deposited in channels and associated levees and overbanks (Figure 1b, d) and consist of interbedding sands, silts, and mudstones. True vertical thickness of reservoir J ranges from 102 to 230 ft (31.1 to 70.1 m). Channel facies have a high N/G ratio of 98%, porosity 27–33%, and permeability 67–5271 md. Levee facies have a smaller and more variable N/G ratio of 13–89%, porosity 28–34%, and permeability 138–837 md.

Petroleum is trapped by a series of normal faults, which separate the reservoir into several fault blocks, and a stratigraphic pinch-out in the northeastern part of the field. Commercial amounts of oil with dissolved gas are found in the central and northern fault blocks (CFB and NFB) of reservoir J (Figure 1d). Wells did not penetrate the OWC in reservoir J. In the CFB, the OWC likely is located at about 12,920 ft (3938 m), where an abrupt decrease in seismic amplitude is observed. The OWC in the NFB is likely about 13,013 ft (3966 m) and is complicated by the southeast-trending shale-filled channel cutting out the J section along strike at about the location of the OWC.

Pressure Data

An approximately 1100-psi (7584-kPa) shift exists between general pressure trends in reservoirs M and J (Figure 2), proving that these reservoirs are not in pressure communication. Additionally, there is an approximately 15-psi (103-kPa) shift between pressures in well MC127#2 (in the CFB) and well MC127#1 (in the NFB) in reservoir J. This difference is significantly larger than the error of measurements (less than 3 psi [20.6 kPa]). Furthermore, unlike the case in reservoir M, the pressure shift cannot be explained by continuous variations in fluid properties. We interpret that pressure data probably indicate a barrier between wells MC127#1 and MC127#2. The WE fault interpreted from 3-D seismic data (Figure 1d) is the most obvious candidate to serve as a barrier between the CFB and NFB of reservoir J.

Basic PVT Properties of Fluids

Reservoir J contains oil with dissolved gas. PVT properties of fluids were determined only in three MDT samples. Similarly to reservoir M, saturation pressure (Psat) in reservoir J shows a large gradient: oils at the bottom of the column are significantly (by as much as ∼2000 psi [∼13.7 MPa]) undersaturated with gas, whereas oils at the top of the column are almost saturated with gas (Figure 4a). The GOR values range from about 960 to 1200 scf/stb and increase at the top of the column. The gradient in GOR is explained by a more significant portion of microbial C1 in the upper part of the column (Figure 4b). The API gravity of dead oils decreases from about 35° in the deepest sampled fluid to about 30° at the top of the column (Figure 4c). This large gradient in API gravity is opposite to what is expected in the well-connected and equilibrated oil column, where lighter petroleum compounds would accumulate at the top of the column. We interpret that the API gravity trend reflects the presence of less mature, relatively heavy oil in the CFB, which has not mixed with more mature, relatively light oil in the NFB separated by the likely impermeable WE fault.

Variations in Fingerprints and Geochemical Composition of Petroleum Fluids

Geochemical data are available from three appraisal wells (including one sidetrack) and one production well. Visual examination of the WOGC from reservoir J reveals that the oil appears similar to the oil from reservoir M (Figure 5). Source-specific biomarkers suggest that oil in reservoir J originated from the same organofacies (marine shales) as the oil in reservoir M (Figures 7, 8). However, noncontaminated oil in the CFB of reservoir J has slightly higher concentrations of sulfur and nickel and higher Pr/n-C17 and Ph/n-C18 ratios than noncontaminated oil in the M reservoir (Table 1). We interpret that oil in CFB of reservoir J is the least mature in the field. This conclusion is further supported by maturity-specific biomarkers (Figure 9). Associated thermogenic gas is also the least mature in the field (Figure 11b). In contrast, both oil (Figure 9) and thermogenic gas (Figure 11b) in the NFB appear more mature than petroleum in the CFB and have the same maturity as petroleum in the NFB of the deeper reservoir M. These differences in maturity within reservoir J result in significantly different oil fingerprints when the ratios of interparaffin peaks in the uncontaminated n-C8.5–n-C11.5 range of WOGC are plotted on a star diagram (Figure 6c, d).

Reliable gas isotope data from MDT are available only for one sample from well MC127#2 in the CFB. This gas contains C1 and ethane (C2) most enriched in 12C relative to the other gases in the field, which results in the largest pull-down of δ13C values in the natural gas plot (Figure 12).We interpret that this gas has the largest portion of microbial gases (both C1 and C2) in the field. Microbial C1 accounts for as much as 96% of total C1 in the upper part of the oil column penetrated by well MC127#2. Methane in the gas separator sample from the nearby downdip producing well BP MCA10 is also enriched in 12C, but not as significantly as the updip sample, which indicates that the concentration of microbial C1 is greatest at the top of the column. Hydrogen isotopes of C1 from the first available separator gases from well A10 further support the greatest abundance of microbial C1 in the CFB of reservoir J (Figure 13).

Although no gas isotope data are available from MDT samples from well MC127#1 in the NFB, we used IsoTube samples to infer that the gas there is more mature (Figure 11b) and contains less microbial C1 (Figure 11a) than the gas from well MC127#2. This is because more mature petroleum with higher thermogenic GOR arrived into the NFB from the deeper M reservoir. Similarly to reservoir M, the concentration of microbial C1 in reservoir J correlates positively with the GOR (Figure 14).

Petroleum Charge (Filling) History

Microbial gas is an important component of petroleum in reservoir J, and we infer that the reservoir contained this gas before the thermogenic petroleum arrived. Arriving undersaturated oil dissolved the microbial gas, similar to the scenario outlined above for reservoir M. Based mostly on the petroleum maturity indicators (Figures 9, 11b), we suggest that oil leaked from the underlying reservoir M and backfilled reservoir J in each individual structural block (Figure 15c, g). The first-arrived oil in the field accumulated first in the CFB of reservoir M (Figure 15d), and it leaked into the overlying CFB of reservoir J when the buoyancy force of increased oil column exceeded the restraining force imposed by the capillary entry pressure in the mudstones of the seal above reservoir M. Oil that leaked from reservoir M to reservoir J in the CFB was the first oil that arrived into reservoir M and was the least mature. The leakage led to the observed distribution of petroleum maturities, where the least mature petroleum at present day occurs in the CFB of reservoir J (Figures 9, 11b).

The NFB of reservoir J likely received petroleum from the underlying NFB of the deeper reservoir M (Figure 15c, g). This is inferred from the similarity of oil and gas maturities in two reservoirs penetrated by well MC127#1 (Figures 9, 11b). Microbial gas was also present in the NFB before the oil arrived.

Reservoir J was charged from different entry points by oil backfilling from the crest of the structure (Figure 15g). In addition to the NFB and CFB being charged separately from the deeper reservoir M, the EFB also received a limited separate charge from reservoir M. Reservoir quality in the EFB is poor. IsoTube data suggest that thermogenic petroleum is present in well MC127#2ST3, but not in the downdip well A9. Apparently, only a small amount of petroleum was able to percolate and backfill the shaly sediments of the EFB. No thermogenic hydrocarbons were found in IsoTube gas samples in wells MC126#1 and MC126#1ST2, suggesting that the oil in reservoir J did not arrive laterally from the west and, thus, confirming our model of vertical migration from reservoir M. We emphasize that the vertical migration of oil occurred by percolation within the mudstones above the crest of reservoir M (i.e., through a migration chimney in Figure 15f) and not through faults, which appear to be sealing and are a critical factor in reservoir compartmentalization.

Compartmentalization Model

Similarly to reservoir M, we used a CRM (Figure 17a) to integrate various available data sets and build a compartmentalization model. Low risk of fluid-flow barriers were inferred between well MC127#1 and its sidetrack BP MC127#1ST1, which is not surprising because these penetrations are located only approximately 40 ft (12 m) apart. Based on limited seismic and pressure data, a moderate risk of barriers was identified between well MC127#2 and its sidetrack BP MC127#2ST2. A moderate risk of barriers is also inferred between well MC127#2 and the downdip producing well A10 because of a dimming in seismic amplitudes (Figure 1d) and slight (but analytically significant) differences in WOGC fingerprints (Figure 6c, d).

Compartmentalization risk matrix (a) and final compartmentalization model (b) for reservoir J of the Horn Mountain field. Appraisal wells are identified with “#,” and only well numbers are shown in (b). Full names of appraisal wells include block number (MC126 or MC127) as listed in (a) (for example, well #2 in b has full name MC127#2 in a). Fluid flow barriers and baffles are interpreted in the same manner as described in Figure 16.

Most importantly, a high risk of barriers was inferred between wells MC127#1 (and its sidetrack) and MC127#2 (and its sidetrack). Seismic data indicate the presence of the WE fault (Figure 1d), and pressure measurements suggest different depth-pressure profiles with a shift of about 15 psi (103 kPa) (Figure 2). Oil fingerprints between the NFB and CFB are significantly different (Figure 6c, d) as a result of very different maturities of oils (Figure 9). The proposed charge model implies separate entry points for oil into these fault blocks (Figure 15). Therefore, evidence from different data sets and the charge model indicates a high risk of fluid flow barriers between wells MC127#1 and MC127#2. The WE fault is an obvious and most likely candidate to serve as a barrier in the reservoir. The fault between the CFB and the EFB also appears to be a fluid-flow barrier because the EFB has a shallower OWC than the CFB (Figure 1d).

The compartmentalization model for reservoir J proposed based on the preproduction data (Figure 17b) has significant implications. Oil in reservoir J is produced from one production well, A10, in the CFB. If the WE fault is a barrier as our model implies, then no oil flow from the NFB to the CFB during production should be expected, and another producer would be needed to recover reserves in the NFB. However, if the WE fault is not a barrier or if it breaks during production as a result of a growing pressure differential between the NFB and the CFB during production, then much of the reserves in reservoir J may be produced by well A10. Therefore, validation and update of the compartmentalization model during production time are critically important for optimizing and managing oil recovery from reservoir J.

CONCEPT OF TIME-LAPSE GEOCHEMISTRY

Time-lapse geochemistry (TLG) is a novel technology that helps to visualize fluid flow during oil and gas production by monitoring changes in fluid compositions across a reservoir. The general idea has been known in petroleum industry for many years, but few published case studies exist. McKinney and Bland (2003) outlined a general TLG approach and an application at the Ursa field (Gulf of Mexico) in a poster. However, to our knowledge, we present here the first detailed description of how TLG was successfully applied and integrated with other reservoir surveillance approaches to provide an understanding of petroleum sweep during production.

In the TLG technology, preproduction and first produced fluid samples are analyzed to define and map fluid types across a static reservoir. The differences in fluid compositions and/or fingerprints are explained by involving fluid flow barriers and baffles, charge model, insufficient mixing time, and/or postaccumulation processes (e.g., biodegradation). Then, a surveillance program in which fluids are sampled from producing wells at regular time intervals is designed and executed. Geochemical compositions and/or fingerprints of obtained production samples are analyzed and compared with preproduction fluids from the same wells and surrounding wells.

Interpretation of TLG data helps to visualize dynamic fluid-flow pathways, determine reservoir areas that do or do not contribute to the production, and recognize broken or continuously sealing fluid-flow barriers and baffles. Information derived from TLG allows a subsurface team to sustain production by placing new wells into unswept parts of the field, avoid drilling new wells in areas that already have been swept, optimize producer and injector pairing, etc. The TLG results should be integrated with results from engineering models, interpretation of 4-D (time-lapse) seismic data, tracers, and other approaches in reservoir surveillance to reduce the uncertainties associated with each individual method.

PRODUCTION SURVEILLANCE OF RESERVOIR M

Oil and associated gas have been produced from reservoir M by seven wells (A1–A5, A8, and A9) since November 2002. Water has been injected in wells A6 and A7 (Figure 1c) since August 2003. The graph in Figure 18 shows how pressure increased in some production wells in response to water injection. Pressure in wells A1 and A4 increased within hours after the water injection started, indicating a good connectivity between those wells and the injectors. Wells A3 and A2 also responded, but not that quickly (within days or weeks), indicating a somewhat poorer connectivity between them and the injectors. However, wells A5, A8, and A9 had little or no response to water injection (Figure 18). Tracers from water injection well A7 reached well A1 in August 2005, and tracers from water injection well A6 reached well A4 in January 2006. No tracers have been recorded in the other producing wells to date (June 2006). It appears from pressure analysis and tracers that the western part of the reservoir forms one flow unit and has energy support from water injectors and likely from the aquifer.

Bottom-hole pressure profiles (simplified and smoothed) for wells producing from reservoir M of the Horn Mountain field. Note how pressure increased in wells A1–A4 after water injection started, but no significant pressure increase was observed in wells A5, A8, and A9.

However, pressure and tracers data do not reveal how oil moves in the eastern part of the field. Most importantly, pressure data provide limited understanding of oil flow around well A8. Our compartmentalization model suggested a moderate risk of barriers around this well (Figure 16), and surveillance data are needed to validate this model.

Four sets of oil samples were collected from reservoir M on December 10, 2003, March 16, 2005, August 1, 2005, and February 11, 2006, which correspond to, respectively, about 13, 28, 33, and 40 months after the startup in November 2002, and to cumulative production from reservoir M of, respectively, approximately 19, 42, 47 and 53 MMBO. Bulk properties and geochemical composition of production oils have little variations, but WOGC fingerprints in n-C8.5–n-C11.5 range provided some valuable insights into oil movements in the reservoir. Plotting WOGC peak ratios on star plots (Figure 19) reveals that fingerprints of oils from well A8 changed most significantly through time. Cluster analysis and principal component analysis of WOGC peak ratios (Figure 20) suggest that

Star plots of 12 most different ratios on interparaffin peaks between n-C8.5 and n-C11.5 in oils collected from producing wells in reservoir M on December 10, 2003 (a), and February 11, 2006 (b). Note that one oil (production sample taken from well A9 on February 11, 2006) served as a duplicate and was run twice (at the beginning and at the end of all GC runs). Relatively poor (visually) reproducibility of peak ratios in the duplicate runs suggests that oils in (b) are actually very similar. Note that the plotted peak ratios are not the same as in Figure 6b because the data set of all production oils and the data set of MDT and first production oils were run separately at different times.

Statistical analysis of WOGC peak ratio data for production oils collected from reservoir M. Cluster analysis (hierarchal, Ward method) dendrogram (left panel) suggests three clusters based on the distance between four duplicate runs of oil collected from well A9 on February 11, 2006. Principal component analysis (right panel, colors of samples are the same as in the dendrogram) helps to further visualize changes in oil fingerprints through time. See text for interpretations. Data are analyzed in statistical package JMP.

oils from wells in the western (A1, A2, A3, and A4) and in the eastern (A5, A8, and A9) parts of the reservoir form two large clusters

the first sample from well A8 forms a separate cluster but later samples join the cluster formed by most A5 and A9 oils

the first oil from well A5 (highlighted by exclamation points in Figure 20) clusters together with oils in the western part of the reservoir, but all later samples cluster with oils from the eastern part of the reservoir

It appears that during continuous production from reservoir M, well A5 started to receive oil from the area of well A9. Well A8 continuously received oil from the downdip part of the reservoir, most likely from the area of wells A5 and A9. Changes in fluid compositions and WOGC fingerprints in the other wells are small. These results suggest that baffles (Figure 16) that prevented complete fluid mixing during petroleum charge apparently transmit fluids during production. The TLG results complement results from the analysis of pressure response to water injection and tracers. Although the analysis of pressure response to water injection and tracer analysis revealed fluid-flow pathways in the western part of reservoir M, TLG results suggested flow pathways in the eastern part of the reservoir. We conclude that there are two flow units in reservoir M, and they were fully recognized only based on integrated analysis of surveillance technologies (Figure 21). These findings would help the asset team to update and validate the reservoir model and to further optimize reservoir management.

Fluid-migration pathways during production of reservoir M as inferred from the analysis of pressure response to water injection and from time-lapse geochemistry.

Original studies (Slentz, 1981; Kaufman et al., 1990) as well as many recent studies (e.g., Noyau et al., 1997; Beeunas et al., 1999) using WOGC fingerprinting for reservoir continuity evaluations assumed that oils from a single continuous reservoir have similar composition and fingerprints, whereas oils from different or discontinuous reservoirs have measurably different compositions and fingerprints. In contrast, Westrich et al. (1999, p. 518) argued against the black-box approach to look at petroleum fingerprinting, suggesting that “underlying causes and controls of observed chemical differences need to be considered and understood for proper fingerprinting.” Our results for reservoir M clearly indicate that wells where oils have different composition and fingerprints can be located in the same production compartment (flow unit). Before production and during initial production, oil in well MC127#3 (A8) was significantly different from oils in the downdip wells MC127#2ST2, A5, and A9 (Figure 6) as a result of slightly lower maturity. However, TLG data suggest that there are no true barriers between well A8 and the downdip wells A5 and A9; all these wells are located in one production flow unit (Figures 19⇑–21). Original differences in oil composition and fingerprints between these wells were set up by the charge history (continuous arrival of oils with increasing maturity) and preserved because of the lack of mixing (as a result of baffles and possibly insufficient time). These results support the argument of Westrich et al. (1999) that differences in oil compositions and fingerprints cannot be straightforwardly interpreted as indicators of reservoir barriers. Integration of geochemical data with modeled petroleum charge history and other nongeochemical data sets is the key to a successful preproduction evaluation of reservoir compartmentalization.

PRODUCTION SURVEILLANCE OF RESERVOIR J

No water injection wells exist in reservoir J, and traditional surveillance approaches are limited to the analysis of production and pressure decline. However, understanding of petroleum sweep by well A10 is critical for reservoir management because it is important to know if this well located in the CFB drains the NFB across the WE fault (Figure 17b).

Five oil samples were collected from producing well A10 (Figure 22a). These samples cover 989 days of production during which about 4.7 MMBO were produced. The star plot of WOGC interparaffin peak ratios (Figure 22b) suggests that the first available sample of production oil from well A10 is slightly different from the MDT oil from the nearby well MC127#2, suggesting the presence of baffles between these two wells. However, WOGC fingerprints in well A10 have not changed significantly during about 3 yr of production (Figure 22b). Similar values of relative standard deviation between four duplicate WOGC runs of one oil and five production samples (Figure 23) reinforce the interpretation that all production oils vary within the error of GC measurements, and there are no statistically significant real differences between them.

(a) Cumulative production of oil, gas, and water from well A10 (reservoir J) and data on samples collected and used in the TLG study. (b) Star plot of 12 most different ratios on interparaffin peaks between n-C8.5 and n-C11.5 in oils from reservoir J. Note that one oil (production sample taken on February 11, 2006) served as a duplicate and was run four times. Samples are listed in the legend in the exact order of WOGC analyses, except for the duplicates that were run between the other samples.

Relative standard deviation values for 30 most different interparaffin peak ratios calculated for four duplicate runs of one oil and five production samples (from December 10, 2003, to February 11, 2006) from well A10 in reservoir J.

The TLG results indicate that probably no oil arrived from the NFB to the existing producing well A10 located in the CFB across the WE fault. Note that TLG data are point measurements and cannot indicate if the oil is flowing from the NFB toward well A10. Nevertheless, this finding helps to justify a proposal to drill another producer in the NFB or to recomplete appraisal wells penetrating this fault block. Additional support for TLG results comes from conventional engineering surveillance approaches. For example, well A10 produces large amounts of water (Figure 22a), which indicates proximity of the aquifer. In addition, production decline analysis suggests that well A10 does not receive contribution from the NFB. Integration of TLG results with conventional surveillance approaches would allow the asset to deliver significant additional reserves in reservoir J and, thus, sustain the declining production at the Horn Mountain field.

ADVANTAGES AND LIMITATIONS OF TIME-LAPSE GEOCHEMISTRY

The Horn Mountain study above is one of the first published applications of TLG in petroleum industry. This technology has many advantages relative to other conventional reservoir surveillance approaches. Similar advantages were noted before for other applications of reservoir geochemical studies (Kaufman et al., 1987, 1990, 1997; Smalley and England, 1992, 1994; Nederlof et al., 1994, 1995; Hwang and Elsinger, 1995; Smalley and Hale, 1996; Nicolle et al., 1997; Westrich et al., 1999). Time-lapse geochemistry is safe because it does not involve much operations and man power. It does not interfere with production, and geochemical analyses are inexpensive, which makes this technology highly cost effective. In contrast to proxy-based approaches such as 4-D (time-lapse) seismic surveillance, TLG is a direct approach of monitoring fluids movement across the reservoir by investigating compositional changes in these fluids. Time-lapse geochemistry may be globally applicable because fluids commonly vary in composition across oil and gas reservoirs as a result of complex charge history, postaccumulation processes, and compartmentalization. Finally, TLG can be performed for reservoirs with various and most complicated well and completion designs because fluids can be collected from well heads and separators at the surface.

The main limitation of TLG is that fluids in the reservoir under surveillance must be measurably different. Preproduction feasibility studies using fluids from downhole tools (e.g., MDT) and production tests (e.g., drill-stem tests, flow tests) aim to establish fluid variability within reservoirs and identify geochemical compounds (natural tracers) that can be used for surveillance. Modern high-resolution gas chromatography can help to distinguish even minor changes in oil compositions based on fingerprinting of interparaffin peaks as was demonstrated in the Horn Mountain study. However, it is possible that in some reservoirs (e.g., small well-mixed oil fields, pure microbial gas fields), fluid differences would be smaller than the reproducibility of geochemical analyses, and TLG would not be applicable to such reservoirs. Geochemical surveillance also may be more complicated or not feasible when production samples are obtained from commingling wells. Finally, application of TLG allows one to see fluids that already arrived to the point of measurements (i.e., producing well), but not the ones that are moving toward it. Thorough integration of various surveillance approaches reduces uncertainties associated with each individual technology and provides the most reliable picture of fluid movements across a reservoir during production.

CONCLUSIONS

Reservoir surveillance is a key activity during production, but conventional approaches have uncertainties. To reduce these uncertainties and maximize the value of surveillance information for reservoir management of the Horn Mountain field in the Gulf of Mexico, we applied TLG surveillance of oil production. Compartmentalization models constructed before production and fluid types mapped based on preproduction MDT and first oil samples served as a baseline for the TLG study. Oil samples from producing wells were collected at semiregular time intervals, and changes in their geochemical compositions and fingerprints were monitored. Interpretation of TLG time-series data and integration with other surveillance approaches allowed us to visualize oil movements within structurally and stratigraphically complex turbidite reservoirs during production, reevaluate reservoir models, and identify unswept parts of reservoirs. The TLG is a safe, noninterventional, and highly cost-effective technology. It involves minimum field operations and greatly reduces the exposure to health, safety, and environment risks associated with the other reservoir surveillance methods.

Acknowledgments

We are thankful to BP America Inc. and Oxy U.S.A., Inc., for permission to publish this study. Thorough analytical work by Baseline (liquid petroleum) and Isotech (gases) is highly appreciated. We thank D. Ebrom and AAPG Bulletin reviewers A. Y. Huc and C. C. Walters for their helpful comments.

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